Amy C. Larson

FPGA implementation of closed-loop control system for small-scale robot

Small robots can be beneficial to important applications such as civilian search and rescue and military surveillance, but their limited resources constrain their functionality and performance. To address this, a reconfigurable technique based on field-programmable gate arrays (FPGAs) may be applied, which has the potential for greater functionality and higher performance, but with smaller volume and lower power dissipation. This project investigates an FPGA-based PID motion control system for small, self-adaptive systems. For one channel control, parallel and serial architectures for the PID control algorithm are designed and implemented. Based on these one-channel designs, four architectures for multiple-channel control are proposed and two channel-level serial (CLS) architectures are designed and implemented. Functional correctness of all the designs was verified in motor control experiments, and area, speed, and power consumption were analyzed. The tradeoffs between the different designs are discussed in terms of area, power consumption, and execution time with respect to number of channels, sampling rate, and control clock frequency. The data gathered in this paper will be leveraged in future work to dynamically adapt the robot at run time

TerminatorBot: a novel robot with dual-use mechanism for locomotion and manipulation

As part of a massively distributed heterogeneous system, TerminatorBot, a novel, centimeter-scale crawling robot, has been developed to address applications in surveillance, search-and-rescue, and planetary exploration. Its two three-degree-of-freedom arms, which stow inside the cylindrical body for ballistic deployment and protected transport, comprise a dual-use mechanism for manipulation and locomotion. The intended applications require a small, rugged, and lightweight robot, hence, the desire for dual use. TerminatorBots unique mechanism provides mobility and fine manipulation on a scale currently unavailable. To facilitate manipulation, we have also developed a specialized force/torque sensor. This new sensor design has a biased distribution of flexures, which equalizes force and torque sensitivities at the operational point. This work describes the mechanism and design of TerminatorBot, the specialized force/torque sensor, and the mechanism-specific gaits.

Communication Strategies in Multi-robot Search and Retrieval: Experiences with MinDART

To explore the effects of different simple communications strategies on performance of robot teams, we have conducted a set of foraging experiments using real robots (the Minnesota Distributed Autonomous Robotic Team). Our experimental results show that more complex communication strategies do not necessarily improve task completion times, but tend to reduce variance in performance.

Performance Evaluation of a Multi-Robot Search & Retrieval System: Experiences with MinDART

Swarm techniques, where many simple robots are used instead of complex ones for performing a task, promise to reduce the cost of developing robot teams for many application domains. The challenge lies in selecting an appropriate control strategy for the individual units. This work explores the effect of control strategies of varying complexity and environmental factors on the performance of a team of robots at a foraging task when using physical robots (the Minnesota Distributed Autonomous Robotic Team). Specifically we study the effect of localization and of simple indirect communication techniques on task completion time using two sets of foraging experiments. We also present results for task performance with varying team sizes and target distributions. As indicated by the results, control strategies with increasing complexity reduce the variance in the performance, but do not always reduce the time to complete the task.

Terrain classification through weakly-structured vehicle/terrain interaction

We present a new terrain classification technique both for effective, autonomous locomotion over natural, unknown terrains and for the qualitative analysis of terrains for exploration and mapping. Our straight-forward approach requires a single camera with little processing of visual information. Specifically, we derived a gait bounce measure from visual servoing errors that result from vehicle-terrain interactions during normal locomotion. Characteristics of the terrain, such as roughness and compliance, manifest themselves in the spatial patterns of this signal and can be extracted using pattern classification techniques. For legged robots, different limb-terrain interactions generate gait bounce signals with different information content, thus deliberate limb motions can effect higher information content (i.e. the robot is an active sensor of terrain class). Segmentation of the gait cycle based on the limb-terrain interaction isolates portions of the gait bounce signal with high information content. The decoding of, then sequencing of, this content from each cycle segment yields a robust classification of terrain type from known benchmarks. To extract this spatio-temporal pattern of the gait bounce signal, we developed a meta-classifier using discriminant analysis and hidden Markov model. We present the gait bounce derivation. We demonstrate the viability of terrain classification for legged vehicles using gait bounce with a rigorous study of more than 700 trials, obtaining 84% accuracy. We describe how terrain classification can be used for gait adaptation, particularly in relation to an efficiency metric. We also demonstrate that our technique is generally applicable to other locomotion mechanisms such as wheels and treads.

Terrain Classification Using Weakly-Structured Vehicle/Terrain Interaction

We present a new terrain classification technique both for effective, autonomous locomotion over rough, unknown terrains and for the qualitative analysis of terrains for exploration and mapping. Our approach requires a single camera with little processing of visual information. Specifically, we derived a gait bounce measure from visual servoing errors that results from vehicle-terrain interactions during normal locomotion. Characteristics of the terrain, such as roughness and compliance, manifest themselves in the spatial patterns of this signal and can be extracted using pattern classification techniques. This vision-based approach is particularly beneficial for resource-constrained robots with limited sensor capability. In this paper, we present the gait bounce derivation. We demonstrate the viability of terrain classification for legged vehicles using gait bounce with a rigorous study of more than 700 trials, obtaining an 83% accuracy on a set of laboratory terrains. We describe how terrain classification may be used for gait adaptation, particularly in relation to an efficiency metric. We also demonstrate that our technique may be generally applicable to other locomotion mechanisms such as wheels and treads.

Evolving gaits for increased discriminability in terrain classification

Limbs are an attractive approach to certain niche robotic applications, such as urban search and rescue, that require both small size and the ability to locomote through highly rubbled terrain. Unfortunately, a large number of degrees of freedom implies there is a large space of non- optimal locomotion trajectories (gaits), making gait adaptation critical. On the other hand, these extra degrees of freedom open many possibilities for active sensing of the terrain, which is essential information for adapting the gait. In previous work, we developed a metric for terrain classification that makes use of the loping body motion (i.e. gait bounce) during locomotion. In this work we present a framework for evolving gaits to better differentiate the gait bounce signal across terrains. This framework includes a limb/terrain interaction model that estimates gait bounce based on established models of wheel/terrain interaction, and an objective function that can be optimized for terrain discriminability. Additional objective functions for improved locomotion are presented, as well as culling agents that help guide the evolution process away from real-world impossibilities.

Using orthogonal visual servoing errors for classifying terrain

A novel, centimeter-scale crawling robot has been developed to address applications in surveillance, search-and-rescue, and planetary exploration. This places constraints on size and durability that minimizes the mechanism. As a result, a dual-use design employing two arms for both manipulation and locomotion was conceived. In a complementary fashion, this paper investigates the dual-use of visual servoing error. Visual servoing can be used by a mobile robot for homing and tracking. But because ground-based mobile robots are inherently planar, the control methodology (steering) is one-dimensional. The two-dimensional nature of image-based servoing leaves additional information content to be used in other contexts. We explore this information in the context of classifying terrain conditions. An outline for gait adaptation based on this is suggested for future work.

Motion estimation with cooperatively working multiple robots

We have investigated the performance of simultaneously estimating the 3D motion and structure for navigation when the scale information is obtained by utilizing the cooperative efforts of multiple robots. The method determines the relative positions of robots by tracking a specific geometric feature that is part of their structure, and then uses the extended Kalman filter to estimate the motion and structure. For implementation we used two CRAWLER Scouts, and performed several experiments to explore the effects of cooperative running of robots on the motion estimation.

Development and User Testing of the Gestural Joystick for Gloves-On Hazardous Environments

For controlling robots in an urban search and rescue (USAR) application, a wearable joystick is presented with improved sensing capability as well as a giant magneto-resistance (GMR) sensor model for use with rare-earth magnets. Scientists have been studying a variety of existing human/robot interface devices to control USAR robots in a disaster. Due to the stresses involved in USAR environments, the selection of an appropriate interface device out of the numerous interactive devices available has to be carefully considered. Furthermore, the total burden to the user of human/robot interface devices in USAR tasks includes not only the periods of interaction, but also the burden of transporting and remotely setting up the devices. The wearable joystick presented is developed with the design goal of minimizing total encumbrances. The features of this wearable joystick include easy and wire-free installation into regular gloves. An improved hardware structure for the sensor pad and the alignment of magnets is described that completely wraps the wrist. This band-type mechanism provides more robust data acquisition than previous prototypes. To evaluate performance, time-to-complete tests are performed, with a comparison to a metric for path tortuosity. The fractal dimension of the resulting path is analyzed to represent the degree of control the user has over the interface device. Experimental results are provided from both computer screen tess and real USAR robot driving tests.